An optimization method for electro-thermal energy management of hybrid aircraft electric propulsion system

Abstract

The present invention relates to a method for optimizing electro-thermal energy management for a hybrid aircraft electric propulsion system, and relates to the field of energy management. The method comprises the following steps: optimizing the hybrid aircraft electric propulsion system using a multidisciplinary optimization design method; constructing an electro-thermal management system for the hybrid aircraft electric propulsion system based on the hybrid aircraft electric propulsion system; the electro-thermal management system comprising a lithium-ion battery electro-thermal coupling model and a motor thermal model, and implementing thermal management of the hybrid aircraft electric propulsion system through thermal management components; and constructing a comprehensive electro-thermal energy management and optimization strategy for the hybrid aircraft electric propulsion system based on MPC, with fuel economy and temperature as optimization targets. MPC rolling optimization control is used to solve the minimum value of the objective function under each flight profile to achieve precise power allocation and temperature control. The present invention enables precise power allocation and effective temperature control of key components.

Description

Technical Field

[0001] The present invention belongs to the field of energy management, and in particular relates to an electric-thermal energy management optimization method for a hybrid power aircraft electric propulsion system. Background Art

[0002] With the global shortage of fossil fuels and increasingly severe environmental issues, issues such as powertrain efficiency, emissions, and noise have drawn significant attention. In recent years, countries around the world have envisioned and formulated future aviation development needs. The European Union launched its Clean Aviation program in 2021, focusing on the development of hybrid-electric regional aircraft, ultra-efficient short- and medium-range aircraft, and hydrogen-powered aircraft. The goal is to have these aircraft operational by 2035 and reduce fuel consumption by 50% and emissions by 90%. Multi- and all-electric aircraft gradually integrate onboard secondary energy sources into electricity, effectively improving aircraft economy, reliability, maintainability, and safety, while reducing pollutant emissions. These efforts have become a key direction in aviation technology development. Therefore, multi-electrification has become a crucial path to enhance aircraft technical and tactical performance and support the development of green aviation.

[0003] Common energy management strategies for aircraft hybrid power systems can be divided into four categories: rule-based, global optimization-based, instantaneous optimization-based, and artificial intelligence-based. Rule-based EMS is highly robust, has low computational complexity, and is easy to implement, but it is difficult to adapt to time-varying operating conditions under high reliability constraints. Global optimization-based EMS can effectively handle multi-objective and multi-constraint optimization problems, but has high computational costs and cannot be applied online. Global optimization-based EMS includes dynamic programming (DP), Pontryagin's minimum principle (PMP), and metaheuristic algorithms. Artificial intelligence-based EMS has good self-learning and adaptability, and can adjust optimization strategies in real time in a dynamic environment. However, it is limited by the aircraft's own case and it is difficult to balance its data dependence and interpretability.

[0004] Therefore, the present invention is aimed at hybrid regional aircraft, and utilizes MPC to perform comprehensive electrical and thermal energy management and optimization of the hybrid aircraft electric propulsion system, thereby achieving precise power distribution and temperature control of key components. Summary of the Invention

[0005] Technical issues to be solved:

[0006] To overcome the shortcomings of the prior art, the present invention provides an electro-thermal energy management optimization method for a hybrid aircraft electric propulsion system. Based on multidisciplinary strong-constraint design, a multi-objective optimization of the hybrid aircraft's "aerodynamics-structure-propulsion" system is performed to obtain the optimized parameters of key components of the electric propulsion system, and a hybrid distributed series propulsion system is designed. Heat dissipation is analyzed using the heat transfer unit method (E-NTU) and a matching component thermal management system is designed. A hybrid thermal management system is constructed by combining a lumped electro-thermal coupling model of lithium-ion batteries, as well as thermal models of drive motors and generators, with energy management strategies. With fuel economy and key component temperatures as optimization objectives, MPC rolling optimization control is used to solve the minimum value of the objective function under each flight profile, thereby achieving precise power distribution and effective control of key component temperatures.

[0007] The technical solution of the present invention is: a method for optimizing electric-thermal energy management of a hybrid aircraft electric propulsion system, the specific steps of which are as follows:

[0008] A multidisciplinary optimization design method is used to optimize the electric propulsion system of a hybrid aircraft. The optimization objective is defined as minimizing fuel consumption and maximum takeoff weight (MTOW). The optimization variables are MTOW, battery weight, and the rated power of the electric propulsion system components. The constraint is the "aerodynamic-structure-propulsion" balance.

[0009] Based on the hybrid aircraft electric propulsion system, an electric-thermal management system for the hybrid aircraft electric propulsion system is constructed; the electric-thermal management system for the hybrid aircraft electric propulsion system includes a lithium-ion battery electric-thermal coupling model and a motor thermal model, and realizes thermal management of the hybrid aircraft electric propulsion system through thermal management components;

[0010] Based on MPC, a comprehensive electric-thermal energy management and optimization strategy for the hybrid aircraft electric propulsion system is constructed. With fuel economy and temperature as optimization targets, MPC rolling optimization control is used to solve the minimum value of the objective function under each flight profile to achieve precise power distribution and temperature control.

[0011] A further technical solution of the present invention is that: the constraints include flight mechanics constraints, weight characteristic constraints, and electric propulsion system component constraints;

[0012] The flight mechanics constraint condition is that the aircraft's flight thrust is equal to the sum of the aircraft's flight resistance and the component of the aircraft's weight in the flight direction;

[0013] The weight characteristic constraint condition is that the maximum takeoff weight of the aircraft is equal to the sum of the aircraft's empty weight, payload weight, fuel mass, and battery pack weight, and the fuel mass is greater than or equal to the used fuel mass;

[0014] The electric propulsion system components include an engine, a generator, a drive motor and a battery pack, with the engine and generator forming a generator set; the constraints are that the sum of the generator output power and the battery pack output power is equal to the drive motor output power; the difference between the total energy capacity of the battery pack and the energy consumed by the battery pack is greater than or equal to 0; the minimum state of charge of the battery pack is less than or equal to the final state of charge of the battery pack.

[0015] A further technical solution of the present invention is that the electric propulsion system component meets the following constraints when the aircraft is in a climbing state:

[0016] The output power of the drive motor is less than or equal to 1.2 times the rated power of the drive motor;

[0017] The turbine engine output power is less than or equal to 1.2 times the turbine engine power;

[0018] The generator output power is less than or equal to 1.2 times the generator power;

[0019] The battery pack output power is greater than or equal to 0.6 times the product of the battery pack weight and the battery pack output power, and less than or equal to the product of the battery pack weight and the battery pack output power.

[0020] The electric propulsion system components meet the following constraints in the cruising state and the landing state:

[0021] The output power of the drive motor is less than or equal to the rated power of the drive motor;

[0022] The turbine engine output power is less than or equal to the turbine engine power;

[0023] The generator output power is less than or equal to the generator power;

[0024] The battery pack output power is less than or equal to the product of the battery pack weight and the battery pack output power, which is equal to the rated power of the battery pack.

[0025] A further technical solution of the present invention is that the lithium-ion battery electrical-thermal coupling model includes a battery pack equivalent circuit model, a battery pack heat generation model, and a battery pack heat dissipation model, and the specific expression is as follows:

[0026]

[0027] Where, Vt is the battery terminal voltage; I L is the load current; R cell is the ohmic internal resistance; V1 is the open circuit voltage; R1 and C1 are the polarization resistance and polarization capacitance respectively; Q bat and T bat are battery temperature and battery heat generation respectively; Q coolant is the heat dissipation of the battery liquid; C p,bat and mbat are the constant pressure specific heat capacity and thermal mass of the battery assembly respectively; C p,coolant and are the specific heat capacity and mass flow rate of the coolant respectively; T bat and T liq,in Respectively represent the battery component temperature and the coolant temperature at the cold plate inlet; h liq is the convective heat transfer coefficient; A liq is the inner surface area of ​​the cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0028] A further technical solution of the present invention is that the motor thermal model includes a drive motor thermal model and a generator thermal model, and the thermal management components used include a coolant reservoir, a circulating pump, a heat exchanger, and a liquid cooling jacket, wherein the drive motor thermal model is composed of the coolant reservoir, the circulating pump, the liquid cooling jacket connected to the drive motor, and the heat exchanger, and the generator thermal model is composed of the fuel tank, the circulating pump, the liquid cooling jacket connected to the generator, and the heat exchanger;

[0029] The expression of the drive motor thermal model is as follows:

[0030]

[0031] Where: Q mot Indicates the heat generation rate of the drive motor; Q coolant is the heat dissipation rate of the liquid flow; c p,mot and m mot Respectively represent the constant pressure specific heat capacity and thermal mass of the drive motor assembly; h liq is the convective heat transfer coefficient; A liq is the inner surface area of ​​the liquid cooling jacket cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0032] The expression of the generator thermal model is as follows:

[0033]

[0034] Where: Q gen Indicates the heat generation rate of the generator; Q coolant is the heat dissipation rate of the liquid flow; c p,gen and m gen Represent the constant pressure specific heat capacity and thermal mass of the generator assembly respectively; h liq is the convective heat transfer coefficient; A liq is the surface area of ​​the liquid cooling jacket; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0035] A further technical solution of the present invention is: the specific steps of constructing the electric-thermal energy integrated management and optimization strategy of the hybrid aircraft electric propulsion system are:

[0036] Build a prediction model for energy management system;

[0037] Build a thermal management system prediction model;

[0038] Construct the objective function;

[0039] Set system constraints;

[0040] The rolling optimization control seeks the minimum value of the objective function.

[0041] A further technical solution of the present invention is: the process of constructing the energy management system prediction model is:

[0042] The output power of the battery group and the generator group at the current sampling time k is selected as the control variable:

[0043] u(k)=[P bat (k),P tg (k)]

[0044] Where, P bat is the output power of the battery pack, P tg is the output power of the generator set;

[0045] Select the lithium battery state of charge SOC and fuel mass m fuel For state variables:

[0046] x(k)=[SOC(k),m fuel (k)]

[0047] The energy management prediction model expression is:

[0048]

[0049] Where: A and B are the state matrix and control matrix of the prediction model respectively;

[0050]

[0051] Where: PSFC is the engine specific fuel consumption; η bat is the charge and discharge efficiency of lithium-ion batteries; E bat,max is the total energy capacity of the battery; △t is the time difference between two adjacent sampling moments, that is, the sampling step.

[0052] A further technical solution of the present invention is: the construction process of the thermal management system prediction model is:

[0053] The speeds of the battery circulation pump, generator circulation pump, and drive motor circulation pump at the current sampling time k are selected as control variables:

[0054] u(k)=[ω pump,bat (k),ω pump,gen (k),ω pump,mot (k)]

[0055] Where: ω pump,bat is the battery circulation pump speed; ω pump,gen is the generator circulation pump speed; ω pump,mot The speed of the driving motor circulation pump;

[0056] The battery pack temperature, generator temperature and drive motor temperature are selected as state variables:

[0057] x(k)=[T bat (k),T gen (k),T mot (k)]

[0058] Where: T bat is the battery temperature; T gen is the generator temperature; T mot is the drive motor temperature;

[0059] The thermal management prediction model expression is:

[0060]

[0061] Where: C and D are the state matrix and control matrix of the prediction model respectively; F(k) is the compensation term;

[0062]

[0063] Where: C bat 、C mot and C gen are the constant pressure specific heat capacity of lithium-ion battery, drive motor and generator respectively; Q bat , Q mot and Q gen are the heat generation rates of lithium-ion batteries, drive motors, and generators respectively; M bat 、M mot and M gen are the thermal masses of lithium-ion battery, drive motor and generator respectively; ω bat0 、ω gmot0 and ω gen0 are the initial speeds of lithium-ion battery, drive motor and generator circulation pump respectively; T bat,liq 、T mot,liq and T gen,liq are the coolant temperatures of lithium-ion battery, drive motor and generator respectively; Tbat0 、T mot0 and T gen0 are the initial temperatures of the lithium-ion battery, drive motor, and generator, respectively.

[0064] A further technical solution of the present invention is: the expression of the objective function is:

[0065]

[0066] Where: N is the prediction time domain length; PSFC is the engine specific fuel consumption; J ems is the energy management objective function; J tms is the thermal management objective function.

[0067] A further technical solution of the present invention is: the system constraints are:

[0068] System output power balance:

[0069] P load (k) = P bat (k)+P tg (k)+P tms (k)

[0070] Where: P load P is the propulsion power demand reference value; tms is the output power of all components in the thermal management system;

[0071] Propulsion system power constraints:

[0072]

[0073] Where: P tg,max is the maximum continuous output power of the generator set, P bat,min 、P bat,max are the maximum charge / discharge power of the battery pack respectively;

[0074] State of charge constraints:

[0075]

[0076] Where: SOC min and SOC max are the minimum and maximum SOC values ​​of the battery pack respectively;

[0077] Key component temperature constraints:

[0078]

[0079] Circulation pump constraints:

[0080]

[0081] Thermal Management Cascade Coupling Constraints:

[0082] Q mot,cool (k)=C1ω pump,mot (k)(T mot0 -T liq,in )≥Q bat

[0083] Where: Q mot,cool is the heat dissipation of the drive motor; Q bat Generates heat for the battery; T liq,in is the coolant temperature at the inlet of the cold plate of the liquid cooling jacket; C1 is the specific heat capacity of the coolant.

[0084] Beneficial effects

[0085] The beneficial effects of the present invention are as follows: the present invention performs multi-objective optimization of the "aerodynamics-structure-propulsion" of hybrid aircraft based on multidisciplinary strong constraint design, can determine the parameters of key components of hybrid aircraft, and play a supporting role in the modeling of hybrid propulsion systems; with the continuous improvement of the requirements for the maneuverability and cruising capabilities of multi / all-electric aircraft, their internal electrical and thermal requirements are further intensified, and a lumped electrical and thermal coupling model of lithium-ion batteries, as well as a thermal model of the drive motor and generator are established, and a hybrid thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system is designed; to ensure the precise distribution of power and the effective control of the temperature of key components, MPC rolling optimization control is used to solve the minimum value of the objective function under each flight profile, thereby improving the reliability of model predictive control and achieving precise power distribution and effective control of the temperature of key components. The specific advantages are as follows:

[0086] (1) The present invention significantly reduces the fuel consumption of the aircraft, reduces operating costs and carbon emissions, and improves the overall performance and reliability of the aircraft by performing multidisciplinary optimization design on fuel consumption and maximum take-off weight (MTOW);

[0087] (2) The present invention constructs an electro-thermal coupling model of the lithium-ion battery and, in combination with the thermal management system of the drive motor and generator, designs an efficient heat dissipation solution to ensure that key components operate at an appropriate temperature, reduce the impact of overheating on system performance and safety, and improve the overall thermal management efficiency;

[0088] (3) By constructing a prediction model for the energy management system and the thermal management system and utilizing the MPC rolling optimization control strategy, the present invention can achieve precise energy distribution under different flight profiles, ensure that each component operates at the optimal working point, and optimize the overall energy efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1Distributed serial propulsion architecture for hybrid regional aircraft.

[0090] Figure 2 This is the electro-thermal coupling model of lithium-ion batteries.

[0091] Figure 3 Thermal model of the drive motor.

[0092] Figure 4 is the thermal model of the generator.

[0093] Figure 5 This is the architecture diagram of the hybrid aircraft thermal management system.

[0094] Figure 6 This is a flow chart of the comprehensive management strategy of electric and thermal energy based on MPC.

[0095] Figure 7 Parametric optimization process for hybrid aircraft.

[0096] Figure 8 Partial weight breakdown comparison of the hybrid aircraft and the reference aircraft.

[0097] Figure 9 The flight mission profile.

[0098] Figure 10 are the ambient temperature and ram air temperature under the full flight profile.

[0099] Figure 11 Flowchart of rule-based energy management strategy.

[0100] Figure 12 Flowchart of rule-based thermal management strategy.

[0101] Figure 13 The power allocation simulation results of the rule-based integrated energy management strategy.

[0102] Figure 14 The power allocation simulation results of the integrated energy management strategy based on model prediction are shown.

[0103] Figure 15 The result is fuel consumption.

[0104] Figure 16 The battery temperature control results of MPC and rule control under the full flight profile.

[0105] Figure 17 The temperature control results of the drive motor under MPC and rule control under the full flight profile.

[0106] Figure 18 The temperature control results of the generator under MPC and rule control under the full flight profile.

[0107] Explanation of the accompanying symbols: 1. Drive motor, 2. Inverter, 3. Battery pack, 4. Generator set, 41. Turbine engine, 42. Generator, 5. Rectifier, 6. Coolant reservoir, 7. Heat exchanger, 8. Liquid cooling jacket, 9. Circulation pump, 10. Fuel tank, 11. Coolant-air heat exchanger, 12. Liquid-liquid heat exchanger, 13. Fuel-air heat exchanger. DETAILED DESCRIPTION

[0108] The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but should not be construed as limiting the present invention.

[0109] Based on the problem of optimizing the energy management strategy of hybrid power systems of conventional aircraft, the present invention is aimed at hybrid regional aircraft, and uses MPC to conduct comprehensive electro-thermal energy management and optimization of the electric propulsion system of hybrid aircraft, so as to achieve accurate power distribution and temperature control of key components. Among them, the instantaneous optimized EMS has good real-time performance and computational efficiency, can quickly respond to dynamic working conditions and achieve local optimal control; the EMS based on instantaneous optimization is widely used in the energy management of aircraft hybrid power systems, mainly including equivalent consumption minimization strategy (ECMS) and model predictive control (MPC). The technical solution adopted by the present invention mainly includes three parts: multidisciplinary optimization design of hybrid aircraft electric propulsion system, modeling of hybrid aircraft electric propulsion system electric-thermal management system and comprehensive electro-thermal energy management and optimization strategy of hybrid aircraft electric propulsion system.

[0110] (1) Multidisciplinary optimization design of hybrid aircraft electric propulsion system

[0111] To evaluate the electric propulsion system configuration at the aircraft level, a multidisciplinary analysis and semi-empirical model combining aerodynamics, aircraft dynamics modeling, propulsion characteristics, and mission analysis was constructed using the OpenMDAO framework. The optimization objectives were fuel consumption and maximum takeoff weight (MTOW), while the optimization variables were MTOW, battery weight, and the power ratings of the propulsion system components (engine, generator, and motor). Constraints were imposed on the "aerodynamic-structural-propulsion" balance. Finally, the Newton-Raphson method was used to determine the optimal parameters for the key components of the electric propulsion system.

[0112] (2) Modeling of the electric-thermal management system of hybrid aircraft electric propulsion system

[0113] An electro-thermal coupling model of the lithium-ion battery, as well as a thermal model of the drive motor and generator, was constructed. The heat transfer unit method (E-NTU) was used to analyze heat dissipation and design a matching component thermal management system. A hybrid thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system was constructed.

[0114] (3) Comprehensive management and optimization strategy of electric-thermal energy in hybrid aircraft electric propulsion systems

[0115] A hybrid aircraft energy management system prediction model and a thermal management system prediction model were established. With fuel economy and key component temperature as optimization targets, MPC rolling optimization control was used to solve the minimum value of the objective function under each flight profile, thereby achieving precise power distribution and effective control of key component temperatures.

[0116] The above technical solution is further described below with reference to the accompanying drawings and specific embodiments:

[0117] In one embodiment, improvement and optimization are performed using a 50-seat regional aircraft as a reference. The specific steps are as follows:

[0118] Step 1: Multidisciplinary Optimization Design of Hybrid Aircraft Electric Propulsion System

[0119] Distributed series propulsion architecture of hybrid regional aircraft Figure 1 As shown, it specifically includes a turbine engine, a generator, a lithium-ion battery pack, a DC / DC converter, an AC / DC rectifier, an inverter and a drive motor.

[0120] To evaluate the configuration of the electric propulsion system at the aircraft level, a multidisciplinary analysis and semi-empirical model of aerodynamics, aircraft power model, propulsion characteristics, and mission analysis was constructed based on the OpenMDAO framework. The optimization parameters of the key components of the electric propulsion system were solved using the Newton-Raphson method. The optimization objectives are fuel consumption and maximum take-off weight (MTOW). The optimization variables are MTOW, battery weight, and the rated power of the propulsion system components (engine, generator, motor). The constraint is the "aerodynamic-structure-propulsion" balance. The aircraft-level model consists of the following three parts: a hybrid propulsion system model, an aerodynamic model, and a whole-machine weight characteristic model. The optimization objectives are as follows:

[0121] min(W fuel +W MTOW )(1)

[0122] Where: W fuel is the fuel consumption; W MTOW is the maximum takeoff weight. The optimization variable is: W MTOW (maximum takeoff weight), W bat (battery pack weight), P mot,rate (rated power of driving motor), P ts,rate (Turbine engine power), P gen,rate (generator power).

[0123] The constraints are as follows:

[0124] 1) Flight mechanics constraints:

[0125] TDW TO sin(γ)=0 (2)

[0126] Where: D is resistance; T is thrust; W TO is the weight of the aircraft; γ is the elevation angle.

[0127] 2) Weight characteristic constraints:

[0128] W MTOW -W OEW -W PL -W fuel -W bat =0 (3)

[0129] W fuel -W fuel,used ≥0 (4)

[0130] Where: W OEW W is the empty weight; PL is the payload weight; W fuel is the fuel mass; W bat is the battery weight; W fuel，used The quality of the fuel used.

[0131] 3) Electric propulsion component constraints:

[0132]

[0133] If the flight state is "climbing state" (6)

[0134] P mot ≤1.2P mot,rated

[0135] P ts ≤1.2P ts,rated

[0136] P gen ≤1.2P gen,rated

[0137] 0.6W bat ·p bat ≤P bat ≤W bat ·p bat

[0138] If the flight status is "cruise state" and "landing state"

[0139]

[0140] Where: P gen is the generator output power; P batis the battery output power; P mot Output power to the drive motor.

[0141] Step 2: Modeling the Electric-Thermal Management System of the Hybrid Aircraft Electric Propulsion System

[0142] Based on the hybrid aircraft electric propulsion system constructed in step 1, an electro-thermal management system for the hybrid aircraft electric propulsion system was constructed. A two-state lumped electro-thermal coupling model was constructed for the lithium-ion battery based on experimental data, and a classical electrical model was constructed for the drive motor and generator based on factory data. This enabled electro-thermal characteristic analysis and optimized design of the system under multiple operating conditions. Combining the electro-thermal coupling model of the lithium-ion battery with the thermal model of the motor, the heat transfer unit method (E-NTU) was used to analyze the heat dissipation process, and a thermal management system was designed to match the battery and motor components. Finally, through basic thermal management components such as liquid cooling plates, circulating pumps, heat exchangers, and liquid reservoirs, the electro-thermal management systems of the battery, drive motor, and generator were combined to construct a highly efficient thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system. The motor thermal model includes a drive motor thermal model and a generator thermal model. The thermal management components used include a coolant reservoir, a circulating pump, a heat exchanger and a liquid cooling jacket. The drive motor thermal model is composed of the coolant reservoir, the circulating pump, the liquid cooling jacket connected to the drive motor, and the heat exchanger. The generator thermal model is composed of the fuel tank, the circulating pump, the liquid cooling jacket connected to the generator, and the heat exchanger.

[0143] 1)Reference Figure 2 As shown, the lithium-ion battery electrical-thermal coupling model

[0144]

[0145]

[0146] Where: V t is the battery terminal voltage; I L is the load current; R cell is the ohmic internal resistance; V1 is the open circuit voltage; R1 and C1 are the polarization resistance and polarization capacitance respectively; Q bat and T bat are battery temperature and battery heat generation respectively; Q coolant is the heat dissipation of the battery liquid; C p,bat and m bat are the constant pressure specific heat capacity and thermal mass of the battery assembly respectively; C p,bat and are the specific heat capacity and mass flow rate of the coolant respectively; T bat and T liq,in Respectively represent the battery component temperature and the coolant temperature at the cold plate inlet; h liq is the convective heat transfer coefficient; A liqis the inner surface area of ​​the cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0147] 2)Reference Figure 3 The thermal model of the drive motor is shown, and the calculation formula is as follows:

[0148]

[0149] Where: Q mot Indicates the heat generation rate of the drive motor; Q coolant is the heat dissipation rate of the liquid flow; c p,mot and m mot Represent the constant pressure specific heat capacity and thermal mass of the drive motor assembly respectively; h liq is the convective heat transfer coefficient; A liq is the inner surface area of ​​the liquid cooling jacket cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0150] Reference Figure 4 The thermal model of the generator is shown, and the calculation formula is as follows:

[0151]

[0152] Where: Q gen Indicates the heat generation rate of the generator; Q coolant is the heat dissipation rate of the liquid flow; c p,gen and m gen Represent the constant pressure specific heat capacity and thermal mass of the generator assembly respectively; h liq is the convective heat transfer coefficient; A liq is the surface area of ​​the liquid cooling jacket; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

[0153] Reference Figure 5 As shown in the figure, the thermal management architecture utilizes a modular hybrid circuit structure, divided into two subsystems: a fuel cooling system and a closed-loop liquid cooling system. 1) In the fuel cooling system, heat absorbed by the fuel is partially discharged to the environment via a heat exchanger and then bypassed by the ram air from the engine. The retained heat can be used to improve engine performance. 2) Cooling loop 1 is responsible for cooling the drive motor. Cooling loop 2 is responsible for cooling the battery pack independently, with the two loops connected in cascade via a liquid-liquid heat exchanger. Because ambient temperature significantly affects the external characteristics of the lithium-ion battery pack, cooling loop 2 is located at the thermal management inlet.

[0154] Step 3: Comprehensive management and optimization strategy of electric-thermal energy of hybrid aircraft electric propulsion system;

[0155] Based on the hybrid aircraft electric propulsion system constructed in step 1 and the hybrid aircraft electric-thermal management system established in step 2, a comprehensive electric-thermal energy management strategy is proposed to achieve optimal allocation and management of system energy utilization efficiency. By comprehensively considering the electric-thermal characteristics of the electric propulsion system components (lithium-ion batteries, drive motors, generators) and the power requirements of the aircraft mission profile, an integrated energy management model adapted to different flight conditions is established. The specific steps are as follows:

[0156] (1) Energy management system prediction model

[0157] The output power of the lithium battery pack and the generator set at the current sampling time k is selected as the control variable:

[0158] u(k)=[P bat (k), P tg (k)] (15)

[0159] The lithium battery state of charge SOC and fuel quality are selected as state variables:

[0160] x(k)=[SOC(k), m fuel (k)] (16)

[0161] The energy management prediction model is as follows:

[0162]

[0163] Where A and B are the state matrix and control matrix of the prediction model respectively.

[0164]

[0165] (2) Thermal management system prediction model

[0166] The speeds of the battery circulation pump, generator circulation pump, and motor circulation pump at the current sampling time k are selected as control variables:

[0167] u(k)=[ω pump,bat (k), ω pump,gen (k), ω pump,mot (k)] (19)

[0168] Where: ω pump,bat is the battery circulation pump speed; ω pump,gen is the generator circulation pump speed; ω pump,mot is the motor circulation pump speed.

[0169] The battery temperature, generator temperature and drive motor temperature are selected as state variables:

[0170] x(k)=[T bat(k), T gen (k), T mot (k)] (20)

[0171] Where: T bat is the battery temperature; T gen is the generator temperature; T mot is the drive motor temperature.

[0172] The thermal management prediction model is as follows:

[0173]

[0174] Where: C and D are the state matrix and control matrix of the prediction model respectively; F(k) is the compensation term.

[0175]

[0176] (3) Objective function construction

[0177] We built prediction models for the energy management system and the thermal management system, taking fuel economy and key component temperatures as optimization targets, and used MPC rolling optimization control to find the minimum value of the objective function under each flight profile, thereby achieving precise power distribution and effective control of key component temperatures. The objective function is as follows:

[0178]

[0179] Where: N is the prediction time domain length; PSFC is the engine specific fuel consumption; J ems and J tms They are energy management objective function and thermal management objective function respectively.

[0180] (4) Setting system constraints

[0181] System output power balance:

[0182] P load (k) = P bat (k)+P tg (k)+P tms (k) (24)

[0183] Where: P load P is the propulsion power demand reference value; bat and P tg are the output power of lithium battery pack and generator set respectively; P tms Represents the output power of all components in the thermal management system.

[0184] Propulsion system power constraints:

[0185]

[0186] Where: P tg,max is the maximum continuous output power of the generator set, P bat,min 、P bat,max are the maximum charge / discharge power of lithium batteries respectively.

[0187] State of charge constraints:

[0188]

[0189] Where: SOC min and SOC max They are the minimum and maximum SOC values ​​of the lithium battery respectively.

[0190] Key component temperature constraints:

[0191]

[0192] Circulation pump constraints:

[0193]

[0194] Thermal Management Cascade Coupling Constraints

[0195] Q mot,cool (k)=C1ω pump,mot (k)(T mot0 -T liq,in )≥Q bat (29)

[0196] Where: Q mot,cool is the heat dissipation of the drive motor; Q bat Generates heat for the battery; T liq,in is the coolant temperature at the inlet of the cold plate of the liquid cooling jacket; C1 is the specific heat capacity of the coolant.

[0197] With fuel economy and key component temperatures as optimization targets, MPC performs rolling optimization control to solve the minimum value of the objective function based on the real-time collected electric-thermal coupling information, the set energy management prediction model, thermal management prediction model, constraints and objective function, thereby achieving precise power distribution and effective control of key component temperatures.

[0198] In one embodiment, an MPC-based integrated electrical-thermal energy management and optimization strategy for a hybrid aircraft electric propulsion system is provided:

[0199] (1) Multidisciplinary optimization design of hybrid aircraft electric propulsion system

[0200] By reading references and referring to the flight parameters of a regional aircraft, the aircraft mission profile parameters are set as shown in Table 1. The hybrid aircraft parameter optimization process is as follows Figure 7shown.

[0201] Table 1 Typical flight profiles for a 1000km range in this case

[0202]

[0203] Reference Figure 7 As shown in Figure 2, the optimization process is as follows: (1) Generate a flight state vector to represent the flight state at each time point during the mission; (2) Calculate the flight atmospheric conditions at different altitudes to obtain multi-scale parameters such as fuel flow, battery state of charge (SOC), thrust, and heat generation; (3) Calculate lift and drag based on the aerodynamic model, and iterate based on the difference between thrust and drag. The Newton-Raphson convergence criteria include three aspects: flight power balance, where thrust and drag are balanced and lift matches weight; energy balance, where the turbine engine shaft power and battery output power meet the power load requirements; and weight balance, where the takeoff weight is consistent with the operating weight, payload weight, and battery weight. Considering the continuous improvement of lithium-ion battery energy density, this study evaluates the impact of battery energy density on hybrid aircraft performance by changing the battery energy density to 600Wh / kg, 800Wh / kg, and 1000Wh / kg. Table 2 shows the comparison of decomposed weight and component power under different battery energy densities.

[0204] Table 2 Comparison of decomposed weight and component power at different battery energy densities in the case

[0205]

[0206] Note: The above engine, generator and motor power / weight are total power / weight.

[0207] As battery energy density increases, the MTOW of hybrid aircraft decreases. This is due to the additional weight of the electric propulsion system caused by using batteries as propulsion energy. Figure 8 The figure shows a comparative analysis of battery weight, fuel consumption, payload, and overall energy density (OEW) for hybrid aircraft with three energy densities compared to a reference aircraft. While maintaining the aircraft's payload, battery weight, fuel consumption, and overall energy density (OEW) all decrease as battery energy density increases. At 600Wh / kg of specific energy, the hybrid aircraft's MTOW increases by 15%, at 800Wh / kg of specific energy, by 12.9%, and at 1000Wh / kg of specific energy, by 11%. Based on the technology level of 2030, the optimized propulsion system parameters for a battery pack with an energy density of 600Wh / kg, as shown in Table 3, were selected as the propulsion system for the hybrid regional aircraft in this study.

[0208] Table 3 Parameter selection of hybrid regional aircraft propulsion system in the case

[0209]

[0210] (2) Modeling of the electric-thermal management system of hybrid aircraft electric propulsion system

[0211] Build a lithium-ion battery electric-thermal coupling model, as well as a drive motor and generator thermal model, such as Figure 2 、 3 As shown in Figure 4. The heat transfer unit method (E-NTU) is used to analyze heat dissipation and design a matching component thermal management system, constructing a hybrid thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system, as shown in Figure 4. Figure 5 shown.

[0212] (3) Comprehensive management and optimization strategy of electric-thermal energy in hybrid aircraft electric propulsion systems

[0213] Set as Figure 9 The physical parameters of the hybrid propulsion system are shown in Table 4. The temperature at the inlet of the ram air heat exchanger is calculated based on the standard atmosphere model and the flight profile, and the ambient temperature and ram air temperature under the flight profile are obtained as follows: Figure 10 shown.

[0214] T env =T env0 -0.0065h (30)

[0215]

[0216] Where: T ram represents the ram air temperature, T env represents the ambient temperature, v represents the flight speed, h represents the flight altitude, and γ represents the air adiabatic index.

[0217] Figure 10 are the ambient temperature and ram air temperature under the full flight profile.

[0218] Table 4 Physical parameters of the hybrid propulsion system in the case

[0219]

[0220]

[0221] The temperature state limits and optimal operating temperature points of each key component are shown in Table 5.

[0222] Table 5 Temperature state constraints and optimal operating temperatures of key components in the case

[0223]

[0224] In order to better verify the feasibility and accuracy of the model, the energy distribution of each power supply and the temperature of key components during the flight of the hybrid aircraft are respectively analyzed through a state machine-based energy management strategy and an MPC-based energy management strategy. The energy distribution methods under different methods are obtained and compared.

[0225] The energy management strategy based on the state machine takes the total load power of the hybrid system and the current SOC value of the lithium battery as input parameters, and formulates the rules of the state machine in combination with the maximum charge and discharge power limit of each power source, the operating range of the lithium battery SOC and other constraints, such as Figure 11 shown. Figure 12 It is a rule-based thermal management process.

[0226] MPC-based integrated management and optimization of electrical and thermal energy Figure 6 As shown, a multi-objective optimization of the hybrid aircraft's "aerodynamics-structure-propulsion" system was performed based on multidisciplinary strong-constraint design to obtain the optimized parameters of the key components of the electric propulsion system. A lumped electrothermal coupling model of the lithium-ion battery, as well as thermal models of the drive motor and generator, was established. The heat dissipation was analyzed using the heat transfer unit method (E-NTU) and a matching component thermal management system was designed. At the same time, the aircraft's multi-energy flow coupling characteristics were analyzed, and a hybrid thermal management system consisting of a closed-loop liquid cooling system and a fuel cooling system was constructed. An energy management system prediction model and a thermal management system prediction model were established. With fuel economy and key component temperatures as optimization objectives, MPC rolling optimization control was used to solve the minimum value of the objective function under each flight profile, thereby achieving precise power distribution and effective control of key component temperatures.

[0227] The simulation sets the initial SOC values ​​of lithium-ion batteries and supercapacitors to 0.5 and 0.7 respectively, and runs for 3600s. The power allocation simulation results of the rule-based energy integrated management strategy are shown in Figure 2. Figure 13 The power allocation simulation results of the energy integrated management strategy based on model prediction are shown in Figure 14 shown.

[0228] like Figure 13 As shown in the figure, during the climbing phase, the generator set power gradually increases linearly until it reaches the maximum power; during the cruising phase, the generator set operates at the optimal power. In the rule-based energy management strategy, the rapid change of propulsion power directly affects the generator set, especially in the climbing phase (about 150 seconds) and the descent phase (about 3000 seconds). The rule-based energy management strategy has inherent defects in dealing with rapid load power changes. Figure 14As shown in Figure 2, the model predicts that the energy management strategy considering TMS maintains a relatively constant turbine generator power level throughout the flight, operating at the optimal power point to maintain the best fuel consumption. The shaded area shows that the generator power fluctuation is smaller than that of STM. The results of fuel consumption are shown in Figure 2. Figure 15 shown.

[0229] like Figure 15 As shown in the figure, the model predictive controller consumes 7.9% less fuel than the rule-based controller, and the model predictive controller keeps the generator set close to the optimal operating PSFC value during most of the flight. Figure 16 The battery temperature control results of MPC and rule control under the full flight profile. Figure 17 The temperature control results of the drive motor under MPC and rule control under the full flight profile. Figure 18 The temperature control results of the generator under MPC and rule control under the full flight profile.

[0230] like Figure 16 As shown in the figure, the rule-based integrated energy management strategy has a throttling effect between the two loops due to the cascade of liquid-liquid heat exchangers between cooling loops 1 and 2, which limits the heat dissipation of loop 2. Therefore, the battery temperature will rise. However, the MPC-based integrated energy management strategy has no significant increase in temperature and always remains at 32°C. Figure 17 As shown in Figure 2, the rule-based integrated energy management strategy causes the circulating pump to frequently start when the drive motor is running at high power, resulting in certain temperature fluctuations ranging from 48.5°C to 52°C. The MPC-based integrated energy management strategy ensures that the drive motor temperature always operates at 50°C. Figure 18 As shown in the figure, the temperature fluctuation of the rule-based integrated energy management strategy is between 49.5℃ and 51.5℃, while the MPC-based strategy relies on the generator temperature prediction model and gives a control command before the temperature exceeds the optimal operating point, so that the generator temperature reaches the optimal operating point around 50℃.

[0231] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention.

Claims

1. A method for optimizing the electric-thermal energy management of a hybrid aircraft electric propulsion system, characterized in that The specific steps are as follows: A multidisciplinary optimization design approach was used to optimize the electric propulsion system of a hybrid aircraft. The optimization objective was defined as minimizing fuel consumption and maximum takeoff weight (MTOW). The optimization variables were MTOW, battery weight, and the rated power of the electric propulsion system components. The constraints were the "aerodynamic-structure-propulsion" balance. Based on the hybrid aircraft electric propulsion system, an electric-thermal management system for the hybrid aircraft electric propulsion system is constructed; the electric-thermal management system for the hybrid aircraft electric propulsion system includes a lithium-ion battery electric-thermal coupling model and a motor thermal model, and realizes thermal management of the hybrid aircraft electric propulsion system through thermal management components; Based on MPC, a comprehensive electric-thermal energy management and optimization strategy for the hybrid aircraft electric propulsion system is constructed. With fuel economy and temperature as optimization targets, MPC rolling optimization control is used to solve the minimum value of the objective function under each flight profile to achieve precise power distribution and temperature control.

2. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 1, characterized in that: The constraints include flight dynamics constraints, weight characteristic constraints, and electric propulsion system component constraints; The flight mechanics constraint condition is that the aircraft's flight thrust is equal to the sum of the aircraft's flight resistance and the component of the aircraft's weight in the flight direction; The weight characteristic constraint condition is that the maximum takeoff weight of the aircraft is equal to the sum of the aircraft's empty weight, payload weight, fuel mass, and battery pack weight, and the fuel mass is greater than or equal to the used fuel mass; The electric propulsion system assembly includes an engine, a generator, a drive motor and a battery pack, and the engine and the generator constitute a generator set; The constraints are that the sum of the generator output power and the battery pack output power is equal to the drive motor output power; the difference between the total energy capacity of the battery pack and the energy consumed by the battery pack is greater than or equal to 0; and the minimum state of charge of the battery pack is less than or equal to the final state of charge of the battery pack.

3. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 2, characterized in that: The electric propulsion system components meet the following constraints when the aircraft is in a climbing state: The output power of the drive motor is less than or equal to 1.2 times the rated power of the drive motor; The turbine engine output power is less than or equal to 1.2 times the turbine engine power; The generator output power is less than or equal to 1.2 times the generator power; The battery pack output power is greater than or equal to 0.6 times the product of the battery pack weight and the battery pack output power, and less than or equal to the product of the battery pack weight and the battery pack output power; The electric propulsion system components meet the following constraints in the cruising state and the landing state: The output power of the drive motor is less than or equal to the rated power of the drive motor; The turbine engine output power is less than or equal to the turbine engine power; The generator output power is less than or equal to the generator power; The battery pack output power is less than or equal to the product of the battery pack weight and the battery pack output power, which is equal to the rated power of the battery pack.

4. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 3, characterized in that: The lithium-ion battery electric-thermal coupling model includes a battery pack equivalent circuit model, a battery pack heat generation model, and a battery pack heat dissipation model. The specific expressions are as follows: Where, Vt is the battery terminal voltage; I L is the load current; R0 is the ohmic internal resistance; V1 is the open circuit voltage; R1 and C1 are the polarization resistance and polarization capacitance respectively; Q bat and T bat are battery temperature and battery heat generation respectively; Q coolant is the heat dissipation of the battery liquid; C p,bat and m bat are the constant pressure specific heat capacity and thermal mass of the battery assembly respectively; C p,coolant and are the specific heat capacity and mass flow rate of the coolant respectively; T bat and T liq,in Represent the battery module temperature and the coolant temperature at the cold plate inlet respectively; h liq is the convective heat transfer coefficient; A liq is the inner surface area of ​​the cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant.

5. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 4, characterized in that: The motor thermal model includes a drive motor thermal model and a generator thermal model. The thermal management components used include a coolant reservoir, a circulation pump, a heat exchanger and a liquid cooling jacket. The coolant reservoir, the circulation pump, the liquid cooling jacket connected to the drive motor, and the heat exchanger constitute the drive motor thermal model, and the fuel tank, the circulation pump, the liquid cooling jacket connected to the generator, and the heat exchanger constitute the generator thermal model. The expression of the drive motor thermal model is as follows: Where: Q mot Indicates the heat generation rate of the drive motor; Q coolant is the heat dissipation rate of the liquid flow; c p,mot and m mot Represent the constant pressure specific heat capacity and thermal mass of the drive motor assembly respectively; h liq is the convective heat transfer coefficient; A liq is the surface area of ​​the liquid cooling jacket cold plate channel; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant; P mot Output power to the drive motor; The expression of the generator thermal model is as follows: Where: Q gen Indicates the heat generation rate of the generator; Q coolant is the heat dissipation rate of the liquid flow; c p,gen and m gen Represent the constant pressure specific heat capacity and thermal mass of the generator assembly respectively; h liq is the convective heat transfer coefficient; A liq is the surface area of ​​the liquid cooling jacket; is the coolant mass flow rate; C p,liq is the specific heat capacity of the coolant, P gen Output power for the generator.

6. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 5, characterized in that: The specific steps of constructing the comprehensive management and optimization strategy of electric-thermal energy of hybrid aircraft electric propulsion system are as follows: Build a prediction model for energy management system; Build a thermal management system prediction model; Construct the objective function; Set system constraints; The rolling optimization control seeks the minimum value of the objective function.

7. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 6, characterized in that: The construction process of the energy management system prediction model is as follows: The output power of the battery group and the generator group at the current sampling time k is selected as the control variable: u(k)=[P bat (k),P tg (k)] Where, P bat is the output power of the battery pack, P tg is the output power of the generator set; Select the lithium battery state of charge SOC and fuel mass m fuel For state variables: x(k)=[SOC(k),m fuel (k)] The energy management prediction model expression is: Where: A and B are the state matrix and control matrix of the prediction model respectively; Where: PSFC is the engine specific fuel consumption; η bat is the charge and discharge efficiency of lithium-ion batteries; E bat,max is the total energy capacity of the battery; △t is the time difference between two adjacent sampling moments, that is, the sampling step.

8. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 7, characterized in that: The construction process of the thermal management system prediction model is as follows: The speeds of the battery circulation pump, generator circulation pump, and drive motor circulation pump at the current sampling time k are selected as control variables: u(k)=[ω pump,bat (k),ω pump,gen (k),ω pump,mot (k)] Where: ω pump,bat is the battery circulation pump speed; ω pump,gen is the generator circulation pump speed; ω pump,mot The speed of the driving motor circulation pump; The battery pack temperature, generator temperature and drive motor temperature are selected as state variables: x(k)=[T bat (k),T gen (k),T mot (k)] Where: T bat is the battery temperature; T gen is the generator temperature; T mot is the drive motor temperature; The thermal management prediction model expression is: Where: C and D are the state matrix and control matrix of the prediction model respectively; F(k) is the compensation term; Where: C bat 、C mot and C gen are the constant pressure specific heat capacity of lithium-ion battery, drive motor and generator respectively; Q bat , Q mot and Q gen are the heat generation rates of lithium-ion batteries, drive motors, and generators respectively; M bat 、M mot and M gen are the thermal masses of the lithium-ion battery, drive motor, and generator respectively; ω bat0 、ω gmot0 and ω gen0 are the initial speeds of the lithium-ion battery, drive motor, and generator circulation pump, respectively; T bat,liq 、T mot,liq and T gen,liq are the coolant temperatures of the lithium-ion battery, drive motor, and generator, respectively; T bat0 、T mot0 and T gen0 are the initial temperatures of the lithium-ion battery, drive motor, and generator, respectively.

9. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 8, characterized in that: The expression of the objective function is: Where: N is the prediction time domain length; PSFC is the engine specific fuel consumption; J ems is the energy management objective function; J tms is the thermal management objective function.

10. The method for optimizing electro-thermal energy management of a hybrid aircraft electric propulsion system according to claim 9, characterized in that: The system constraints are: System output power balance: P load (k)=P bat (k)+P tg (k)+P tms (k) Where: P load P is the propulsion power demand reference value; tms is the output power of all components in the thermal management system; Propulsion system power constraints: Where: P tg,max is the maximum continuous output power of the generator set, P bat,min 、P bat,max are the maximum charge / discharge power of the battery pack respectively; State of charge constraints: SOC min ≤SOC(s)≤SOC max Where: SOC min and SOC max are the minimum and maximum SOC values ​​of the battery pack respectively; Key component temperature constraints: Circulation pump constraints: Thermal Management Cascade Coupling Constraints: Q mot,cool (k)=C1ω pump,mot (k)(T mot0 -T liq,in )≥Q bat Where: Q mot,cool is the heat dissipation of the drive motor; Q bat Generates heat for the battery; T liq,in is the coolant temperature at the inlet of the cold plate of the liquid cooling jacket; C1 is the specific heat capacity of the coolant.

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